ABSTRACT

This is one of the first parallel computing books to focus exclusively on parallel data structures, algorithms, software tools, and applications in data science. The book prepares readers to write effective parallel code in various languages and learn more about different R packages and other tools. It covers the classic n observations, p variables matrix format and common data structures. Many examples illustrate the range of issues encountered in parallel programming.

chapter 3|44 pages

Principles of Parallel Loop Scheduling

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chapter 5|42 pages

The Shared-Memory Paradigm: C Level

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chapter 6|22 pages

Overview

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chapter 7|10 pages

Thrust and Rth

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chapter 8|18 pages

The Message Passing Paradigm

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chapter 9|12 pages

MapReduce Computation

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chapter 10|14 pages

Parallel Sorting and Merging

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chapter 11|18 pages

Parallel Prefix Scan

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chapter 12|24 pages

Parallel Matrix Operations

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